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| import noisereduce as nr | |
| import scipy.io.wavfile as wavfile | |
| import numpy as np | |
| import gradio as gr | |
| import os | |
| import tempfile | |
| import shutil | |
| def denoise_audio_file(input_path, output_path): | |
| rate, data = wavfile.read(input_path) | |
| if len(data.shape) > 1: | |
| reduced_noise = np.zeros_like(data, dtype=np.float32) | |
| for channel in range(data.shape[1]): | |
| reduced_noise[:, channel] = nr.reduce_noise(y=data[:, channel], sr=rate) | |
| else: | |
| reduced_noise = nr.reduce_noise(y=data, sr=rate) | |
| wavfile.write(output_path, rate, reduced_noise.astype(data.dtype)) | |
| return output_path | |
| def process_single_file(file): | |
| if not file.name.endswith('.wav'): | |
| raise gr.Error("Please upload a WAV file") | |
| # Use the original filename for the denoised file, but in a temp dir | |
| name, ext = os.path.splitext(os.path.basename(file.name)) | |
| base_filename = f"{name}_denoised{ext}" | |
| temp_dir = tempfile.mkdtemp() | |
| output_path = os.path.join(temp_dir, base_filename) | |
| denoise_audio_file(file.name, output_path) | |
| return output_path | |
| def process_batch_files(files): | |
| output_files = [] | |
| temp_dir = tempfile.mkdtemp() | |
| for file in files: | |
| if file.name.endswith('.wav'): | |
| name, ext = os.path.splitext(os.path.basename(file.name)) | |
| base_filename = f"{name}_denoised{ext}" | |
| output_path = os.path.join(temp_dir, base_filename) | |
| denoise_audio_file(file.name, output_path) | |
| output_files.append(output_path) | |
| return output_files | |
| with gr.Blocks(title="Audio Noise Reducer") as demo: | |
| gr.Markdown("# 🎧 Audio Noise Reduction") | |
| gr.Markdown("Upload WAV files to remove background noise using AI-powered processing.") | |
| with gr.Tab("Single File Processing"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| single_file = gr.File(label="Upload WAV File", file_types=[".wav"]) | |
| single_btn = gr.Button("Process File") | |
| with gr.Column(): | |
| single_output = gr.File(label="Download Denoised File") | |
| single_status = gr.Textbox(label="Processing Status", interactive=False) | |
| single_btn.click( | |
| fn=process_single_file, | |
| inputs=single_file, | |
| outputs=single_output, | |
| api_name="process_single" | |
| ) | |
| with gr.Tab("Batch Processing"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| batch_files = gr.File(label="Upload WAV Files", file_count="multiple", file_types=[".wav"]) | |
| batch_btn = gr.Button("Process Files") | |
| with gr.Column(): | |
| batch_output = gr.Files(label="Download Denoised Files") | |
| batch_status = gr.Textbox(label="Processing Status", interactive=False) | |
| batch_btn.click( | |
| fn=process_batch_files, | |
| inputs=batch_files, | |
| outputs=batch_output, | |
| api_name="process_batch" | |
| ) | |
| demo.queue() | |
| if __name__ == "__main__": | |
| demo.launch() | |